Federated deep transfer learning for EEG decoding using multiple BCI tasks. (arXiv:2211.10976v2 [eess.SP] UPDATED)

Deep learning has been successful in BCI decoding. However, it is very
data-hungry and requires pooling data from multiple sources. EEG data from
various sources decrease the decoding performance due to negative transfer.
Recently, transfer learning for EEG decoding has been suggested as a remedy and
become subject to recent BCI competitions (e.g. BEETL), but there are two
complications in combining data from many subjects. First, privacy is not
protected as highly personal brain data needs to be shared (and …

arxiv bci decoding eeg multiple transfer transfer learning